The present disclosure relates to biometric identification, and more particularly, to an apparatus and method of biometric object spoof detection based on image intensity variations.
Within the field of computing, many scenarios involve an identification of an individual using one or more biometrics. In one example, for instance, iris recognition is considered as one of the most secure forms of biometric authentication and verification. With cameras becoming smaller, products are now available in the market that use iris recognition as a primary mode of authentication to secure all the data on the device they intend to protect. With the ability for anyone to take a picture of your face with high fidelity, the possibility of spoof attacks has increased. This makes anti-spoofing more important, and a harder problem to solve.
Current solutions for iris spoof detection, may be grouped into 3 classes:
(1) Analysis of iris texture pattern to distinguish a printed iris or real one. This is a popular approach, however, a con of this solution is that it is possible to print out high quality iris pattern because recent 2D/3D printers have high quality and high-usability to spoof iris recognition systems.
(2) Use of an additional device, e.g. combining an infrared (IR) camera and a red, green blue (RGB) camera or depth sensor. This approach may use human skin or face structure information to detect spoofing. Some cons of this solution, however, are that it is more expensive, it needs space to attach the additional device, and it may not work in low-light environments (e.g. if RGB camera is used).
(3) Detecting movements—In one implementation, such solutions may detect uncontrollable movements, for example, humans cannot stop eye movements called “saccadic suppression.” In other implementations, such solutions may ask users to take unique actions, e.g., blinking, looking away or turning their face. A con of these approaches is that they can take extra time (e.g., extending the time to log in), they may be bothersome for users to take explicit actions, and/or they may destroying the ‘magic’ of biometric by requiring explicit actions.
Further, when utilizing such biometric identification systems in a mobile computing device, the constraints get much harder than in stationary systems due to the potential of independent movement between the mobile computing device and the user.
Thus, there is a desire for improvements in the field of identification of an individual using one or more biometrics.